Detection of Vulnerabilities Related to Permissions Requests for Android Apps Using Machine Learning Techniques

Bassolé, Didier; Traoré, Yaya; Koala, Gouayon; Tchakounté, Franklin; Sié, Oumarou

doi:10.1007/978-3-030-73689-7_77

Cited by 1 publication

(2 citation statements)

References 29 publications

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“…The consequences of this malware for users are enormous. On an ongoing basis, a number of researchers have proposed methods and techniques for detecting malware in applications [7], [8], [17], [18], [10]. The aim is to improve data protection methods by reducing threats and attacks against private data.…”

Section: Related Workmentioning

confidence: 99%

“…Numerous other works on detecting malware in Android applications extract certain information to distinguish malicious applications from benign ones. This is the case of the work by Bassole et al [18] and the authors [8] and [9]. These authors extracted authorisations and other functionalities as features, and combined them with machine learning methods to detect malware.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Software Vulnerabilities’ Detection by Analysing Application Execution Traces

Koala,

Bassol´e,

Tiendrebeogo

et al. 2023

IJACSA

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Over the years, digital traces have proven to be significant for analyzing IT systems, including applications. With the persistent threats arising from the widespread proliferation of malware and the evasive techniques employed by cybercriminals, researchers and application vendors alike are concerned about finding effective solutions. In this article, we assess a hybrid approach to detecting software vulnerabilities based on analyzing traces of application execution. To accomplish this, we initially extract permissions and features from manifest files. Subsequently, we employ a tracer to extract events from each running application, utilizing a set of elements that indicate the behavior of the application. These events are then recorded in a trace. We convert these traces into features that can be utilized by machine learning algorithms. Finally, to identify vulnerable applications, we train these features using six machine learning algorithms (KNN, Random Forest, SVM, Naive Bayes, Decision Tree-CART, and MLP). The selection of these algorithms is based on the outcomes of several preliminary experiments. Our results indicate that the SVM algorithm produces the best performance, followed by Random Forest, achieving an accuracy of 98% for malware detection and 96% for benign applications. These findings demonstrate the relevance and utility of analyzing real application behavior through event analysis.

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